class_docstr: A class for converting a dataframe and/or url to a vectorstore embedded with a given embeddings model
class_docstr_tok: ["A","class","for","converting","a","dataframe","and\/or","url","to","a","vectorstore","embedded","with","a","given","embeddings","model"]
code: def __init__(
      self,
      args: RAGBaseParameters,
      df: pd.DataFrame,
  ):
      self.args = args
      self.df = df
      self.embeddings_model_name = args.embeddings_model_name
  
      self.vector_store = VectorStoreFactory.get_vectorstore_class(
          args.vector_store_name
      )
  
file_path: mindsdb/mindsdb/integrations/handlers/rag_handler/ingest.py
imports: import time
  from typing import List
  import pandas
  from langchain.schema import Document
  from langchain.text_splitter import RecursiveCharacterTextSplitter
  from langchain.vectorstores import VectorStore
  from mindsdb.integrations.handlers.rag_handler.settings import PersistedVectorStoreSaver, PersistedVectorStoreSaverConfig, RAGBaseParameters, VectorStoreFactory, df_to_documents, get_chroma_client, load_embeddings_model, url_to_documents
  from mindsdb.utilities import log
label_desc: Use image node_id 1 to create a new RAGIngestor object  with example: obj = RAGIngestor(args, df)